Accordingly, the invention disclosed in the present patent application concerns such a method which provides improved patient safety by identifying emergency situations related to an anesthetic system prior to the occurrence of a real accident situation. By virtue of the method, the user can easily recognize the existence of an emergency situation and the origin of such an emergency situation at an earliest possible stage before an actual injury to the patient is caused. In the context of the present patent application, the term "anesthesia system" is used denoting the entity formed by the patient and the equipment connected to the patient during anesthesia.
Risks associated with anesthesia have been assessed in a plurality of investigations, and the results obtained therein have varied according to the patients' age distribution and health condition of the patients, time of the investigation and geographical location of hospitals participating in the investigation. Actually, the only conclusion that can be drawn from the results is that the risks associated with anesthesia today are relatively small. For instance, the summary compiled by Derrington in 1987 on the research results from anesthesia risk assessments revealed the risk associated with anesthesia to be maximally 22 death cases in 10,000 anesthesias (Derrington, M. C., and Smith, G. "A review of studies of anaesthetic risk, morbidity and mortality", British Journal of Anaesthesia 59, pp. 815-833, 1987). In most of the investigations, the actual risks were even below this, and for example, according to a study performed in 1986 in Turku, Finland, the corresponding anesthetic risk level in Finland was on the average only approx. 0.61 death cases in 10,000 anesthesias (Tikkanen, J. "Ariestesla-ja leikkaustoimenpiteisiin liittyvat kuolemat Suomen sairaaloissa v. 1986 (title in English: "Death cases associated with anesthetic and surgical operations in Finnish hospitals in 1986"), Doctoral thesis, Turku university, 1992.)
Notwithstanding the low risk level of anesthesias, each patient injury or death caused by anesthesia is excessive. To recognize developing emergency situations and eliminate them earliest possible phase before the occurrence of an injury, the patient status is today supervised by means of values measured and computed by different kinds of monitoring equipment, typically displayed by the monitoring equipment in the form of numerical values or graphs. Some of the most common examples of such monitored variables are blood pressure, ECG, blood oxygen saturation level and numerical values and graphs related to the composition, pressure and flow rate of the gas mixture inspired by the patient. Different patient complications, incorrect use of equipment and actual fault situations of the equipment are then reflected as changes in the values measured and computed by the monitoring equipment.
The wider the spectrum of numerical values and graphs crucial to the recognition of emergency situations made available to the anesthesia personnel, the better the possibilities, at least in theory, of recognizing developing emergency situations. In practice the patient and the anesthesia apparatus during the anesthesia form a very complex and almost inseparable entity. Therefore, it is extremely difficult to deduce the existence of an emergency situation and its cause on the basis of numerous curves and values shown on the monitor screens. While deduction capability increases with personnel training, experience and improved user interfaces, at some point a limit is reached after which the addition of more values and graphs on the displays no longer improves patient safety. Also the almost infinite number of acceptable situations hampers the recognition of emergency situations at their early stage. For example, no ideal set values can be given to an anesthesia unit, but rather, the tidal volume, pressure, gas mixture composition, etc., van according to the patient's weight, age, sex, operating posture, physical condition, type of operation and a great number of other parameters. While measurement results clearly deviating from acceptable values can be easily noticed, the difficulty of identifying the cause of the emergency situation remains.
The occurrence of an emergency situation is facilitated by upper and lower limit alarms, which today are programmed in most anesthesia monitoring equipment. Such upper and lower alarm limits are defined for each variable measured or computed by the equipment from the measurement values, and when the measured values fail to stay between the set alarm limits, the monitoring equipment issues an alarm. If the upper and lower alarm limits are set very close to each other, most of the issued alarms are false. Though the use of tightly set alarm limits may in principle permit the recognition of a developing emergency situation at a relatively early stage, the great number of false alarms tends to prevent the identification of emergency situations in practice. Annoyed by such false alarms, the personnel often resorts to indeed entirely disabling the alarms (Kerr, J. H. "Warning devices", British Journal of Anaesthesia, 57, pp. 696-708, 1985).
Accordingly, the problems of false alarms are solved in a great number of conventional monitoring equipment by defining the default values of alarm limits so wide that false alarms are practically nonexistent with the exception of the start and end phases of anesthesia. If the measured values, however, ultimately violate the upper or lower alarm limit, the patient may in the worst case have already been subjected to an injury, and even in the most favorable cases, the time remaining to salvage the patient may be extremely marginal. In such a situation it is of invaluable importance that the anesthesia personnel can rapidly identify the cause of the emergency situation. Such cause of the emergency situation is not identified by the limit alarms, which rather only provide information on which variable(s) has/have violated the set alarm limit(s).
Construction of more intelligent systems capable of identifying emergency situations has been attempted by way of evaluating the measurement values with analytical means, and then using the found correlations to develop simple rule-based systems and expert systems (Jiang, A. "The design and development of a knowledge-based ventilatory and respiratory monitoring system", Doctoral thesis, Graduate School of Vanderbilt University, 1991; and Nederstigt, J. A. "Design and implementation of second prototype of the intelligent alarm system in anesthesia", Diploma Engineer's (M.Sc.) thesis, Eindhoven University of Technology, 1991), while also these systems require an explicit definition of the variable values used in the identification process. However, the number of measured variables is large, and the correlations between the variables are difficult to establish, further complicated by the arbitrariness of judgment between a normal and an abnormal situation. Moreover, the identification of emergency situations is hampered by the different behavior of the measured variables at different operating points of the anesthesia unit. The term "operating point" is used herein denoting such a pattern vector which corresponds to the normal situation with, e.g., predefined anesthesia unit settings and predefined patient. Not even expert systems are particularly well suited to handling such slightly fuzzy relationships, causing them to grow to extremely complicated dimensions with a slow response and rather massive structure.
Westenskow has in his patent application (titled "Device and method for neural network breathing alarm", Westenskow, D., Salt Lake City, filed under patent application no. PCT/US90/05250, 14.9.1990(4.4.1991), 44 pp.) tried to solve the above-described problems by way of an artificial neural network based on error backpropagation. Conventionally, the term "artificial neural network" is used to denote networks formed by parallel cells in which a great number of functionally simple cells are connected to each other. The cells are generally adaptive, or learning, and in a system based on artificial neural networks, the analysis of measurement value changes associated with emergency situations is left to the neural network. Through such a process, the neural network learns to differentiate normal and abnormal situations from each other. Simultaneously, the network learns the mutual relationships of the measured variables in different situations, whereby the cause of the emergency situation is identified in addition to the recognition of the emergency situation.
Artificial neural networks are particularly suited to different kinds of pattern recognition tasks. Thus, they have been successfully used in, e.g., speech recognition, hand-written text recognition, texture recognition and robotics. Pattern recognition by means of artificial neural networks has in the prior art also been tested in different kinds of operating condition monitoring tasks to which both the method of Westenskow and the method disclosed in this patent application basically belong: differently from industrial processes, in anesthesia man simply replaces one of the components in the system being monitored.
In the recognition of emergency situations associated with anesthesia, the measurement results are processed into pattern vectors which the artificial neural network then classifies as either a normal situation or an emergency situation with simultaneous identification of the cause of the emergency situation. The different components of the pattern vector are later denoted as "features". The pattern vectors associated with different emergency situations are taught to the neural network prior to the actual identification process, and to obtain sample situations used for training the system, Westenskow has used both a respiration simulator and test animals. During the collection of the sample situations, the emergency situations to be identified are repeated as many times and with as many settings of the anesthesia unit as possible. The features computed from the measurement results obtained from emergency situations are taught to the neural network through a separate learning process. Then, the neural network learns to identify also such pattern vectors which differ from the initial training vectors taught to the neural network, while still bearing a similarity, thus permitting the neural networks during the identification of emergency situations to perform a generalization from the samples taught to the system. Thus, the above-discussed learning process which is also described in Westenskow's patent application is by no means novel: a corresponding process is earned out when any neural network is used in any possible application.
The cause of an emergency situation is identified according to Westenskow's patent application by comparing the measurement value of a given instant of time to the value obtained during the preceding measurement session, whereby the dependence of the features used for identification on, e.g., the settings of the anesthesia unit is reduced. However, changes in the anesthesia unit settings and normal physiological changes in the patient cause false alarms in a system based on recognition of changes, while hazardous changes which proceed gradually remain unrecognized. To remove such false alarms, Westenskow presents in his patent application a solution in which the presence of an emergency situation is first recognized by means of an error backpropagation neural network, and only when such an emergency situation is recognized, the cause of the emergency situation is identified by means of another, similar type of neural network.
However, the invention disclosed by Westenskow has several drawbacks, most of which are related to the type of neural network employed. A discussion of the drawbacks associated with the solution is given below:
1. Determination of uncertainty level for the identification result is not reliable by means of a backpropagation neural network in the case where the pattern vector to be identified fails to resemble any of the training vectors taught to the neural network. Then, an entirely incorrect identification result may plausibly have even more hazardous consequences than a message reporting that identification is most probably not possible at all.
2. As measurement results from both normal situations and emergency situations must be used in the training of the neural network used for recognizing an emergency situation, training by the user in actual clinical conditions is not possible. Further, the average measurement values obtained during different kinds of operations can vary significantly from each other: For example, higher than normal tidal volumes are used in neurosurgical operations, which further results in a fall of the expiratory air CO.sub.2 significantly below the values used in normal anesthesia. The tidal volume and respiration rate employed also vary depending on the patient's size and the preferences of the anesthesiologist.
3. Giving an explanation for the reasons of the identification result to the user is extremely difficult if not even impossible. This inherent property of artificial neural networks is frequently criticized, particularly in conjunction with the identification of emergency situations related to anesthesia. In fact, for a general case a neural network appears as a black box: a pattern vector is taken to the input of the network and the output of the network provides an identification result, while nobody has an exact information on the grounds on which the neural network ended up in the final conclusion, and therefore, also user reservations on the reliability of the conclusion are easily launched.
4. After the measurement results have permanently settled to the level of the emergency situation, identification of the cause of the emergency situation is no longer possible.
5. Identification of causes of gradually developing emergency situations is not possible.